Formal analysis of the quality and conformance of information flow downgraders

ABSTRACT

Mechanisms for evaluating downgrader code in application code with regard to one or more security guidelines are provided. Downgrader code in application code is identified, where the downgrader code is a portion of code in the application code that operates on an information flow of the application code to ensure confidentiality of information input to the downgrader code, in the output of the downgrader code. Processes of the downgrader code are evaluated against security guidelines to determine if the processes violate the security guidelines. A notification is generated in response to the evaluation indicating that the processes of the downgrader code violate the security guidelines. The notification is output to a computing device for consideration.

This application is a continuation of application Ser. No. 12/968,646, filed Dec. 15, 2010, status pending.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for static enforcement of application security guidelines by using formal analysis of the quality and conformance of information flow downgraders.

The Information-Flow Security principle establishes that no “illicit flow” of information is allowed in a program. A flow is illicit if it allows untrusted information to be used in a trusted computation (an integrity violation) or if it allows secret information to be entirely or partly revealed to unauthorized users (a confidentiality violation). Integrity and confidentiality can be seen as dual problems by simply stating that there should not be any flow of information from “high” to “low”, where “high” means “untrusted” in integrity and “secret” in confidentiality, and low means “trusted” in integrity and “public” in confidentiality.

Information can be tagged with information flow labels. Typically, information flow labels form a partially ordered set or a lattice. If information-flow security was strictly enforced and no illicit flow of information was allowed, most programs would not work. To be “information-flow secure,” a program would have to be “partitioned” so that information tagged with a certain label “X” can only flow to program points that have been tagged with labels higher than or equal to “X”.

A program with these restrictions is very unlikely to be useful. For example, from an integrity point of view, a Web application is supposed to accept inputs from potentially untrusted users and use those inputs in trusted computations. For example, an online banking program takes as input the account number and the password of a user (potentially untrusted or malformed information) and passes them to the backend database system where they are used in a trusted setting. In another example, an online bookstore takes as input the user ID and password of the customer and the title of the book that the customer wants to buy (all potentially untrusted or malformed information), and uses them to complete a transaction, etc.

From a confidentiality point of view, a Web application often releases data that has been computed based on secret information and, as such, should be considered secret as well. For example, a banking application may reveal to any teller the last four digits of the social security number of any user, an online bookstore may reveal to any shop assistant the last four digits of any customer's credit card number, etc. Given that all these programs exhibit flows that allow “high” information to flow to “low” program points, all these programs would be rejected if information-flow security were simply enforced. To permit these programs to function, “high” information can be “downgraded” and become “low” enough to be used in “low” program points.

Downgrading translates itself into “endorsement” in integrity and “declassification” in confidentiality. For example, once a program has verified that the user-provided input to a Web application is a properly formatted string, the program can endorse that input, which now becomes trusted enough to be used in a trusted computation. Similarly, once a program has verified that the information extracted from a secret is not sufficient to reveal the secret itself, the program can declassify the extracted information, which now can become public enough to be revealed to a public listener.

A program can implement many different types of downgraders. That is, these downgraders are used because a program should not accept any “high” input to a “low” function unless that “high” input has been previously downgraded. A particular downgrader operates for a particular specific subset of the set of “low” functions and thus, a program may be required to implement a plurality of different types of downgraders.

For example, an integrity “low” function that accepts an input in the form of a string, concatenates that string into a Structured Query Language (SQL) query, and then submits it to a database. In this example, the function will require its input not to contain semicolons and apostrophes, since such characters will be interpreted by the database as SQL commands. Therefore, any input to this “low” function should have undergone sanitization (i.e. transformation of an illegal input by removing/replacing suspect parts of the illegal input) or endorsement, to make sure that such illicit characters are not there. Only if a trusted sanitizer has verified the absence of such illicit characters will that initially untrusted string be accepted to be used in the SQL query.

However, if the “low” function is not responsible for performing SQL queries, but rather for concatenating its string input value into HyperText Markup Language (HTML) code, then a different sanitization is necessary. The issue here is no longer to prevent SQL injections, but rather to prevent what are known as Cross-Site Scripting (XSS) attacks. In this case, the sanitization function must check for absence of specific JavaScript tags, such as <script> and </scripts>.

Downgraders are often available in libraries, and are categorized based on the specifications of the corresponding “low” functions. Often, however, Web applications implement their own downgrading functions. This makes security static analysis of Web applications very complex. In fact, a static analysis for information-flow security should receive as input the signature of the downgrading functions as well as rules that map downgrading functions to the corresponding “low” functions. At that point, the static analysis can verify whether the input to a “low” function has always undergone proper downgrading, without any path leading to a “low” function unless its inputs have been properly downgraded. Unfortunately, when Web applications implement their own downgraders, it is very difficult to detect those downgraders and categorize them in a way that the static analysis for information-flow security can subsequently account for them.

Web applications are particularly vulnerable to security attacks because they feed on user input and are typically accessible by a large number of users. According to the Web Application Security Consortium (WASC), approximately 100,000 security vulnerabilities were found an fixed in 2008 with 52,587 of these vulnerabilities being either urgent or critical. This illustrates the importance of protecting Web applications against malicious inputs. This protection is typically implemented using the endorsement/downgrader mechanisms previously described above which either sanitize the user's input (i.e. transform the input by removing/replacing suspect parts of the input) or validate the user's input (i.e. reject the user's input if it is judged to be illegal).

Sanitizers and validators can be thought of as the last (and most application-specific) line of defense against attacks. These mechanisms usually embody subtle reasoning which is meant to distinguish between legal and illegal inputs in various contexts. Moreover, these mechanisms themselves are the interface between the security experts and the application developers. Writing them correctly is not a standard coding task as a thorough understanding of security threats (in the form of the long catalogue of known security attacks) is required. Best practices, guidelines, and policies on how to create sanitization and validation mechanisms are often found in security documents.

The challenge is to check whether these guidelines are followed in the code of the Web application. There is currently no automated mechanism to carry out this check.

SUMMARY

In one illustrative embodiment, a method, in a data processing system, is provided for evaluating downgrader code in application code with regard to one or more security guidelines. The method comprises identifying, by an application analysis mechanism of the data processing system, the downgrader code in the application code. The downgrader code is a portion of code in the application code that operates on an information flow of the application code to ensure confidentiality of information input to the downgrader code, in the output of the downgrader code. The method further comprises evaluating, by the application analysis mechanism, one or more processes of the downgrader code against the one or more security guidelines to determine if the one or more processes of the downgrader violate the one or more security guidelines. Moreover, the method comprises generating, by the application analysis mechanism, a notification in response to the evaluation indicating that the one or more processes of the downgrader code violate the one or more security guidelines. In addition, the method comprises outputting, by the application analysis mechanism, the notification to a computing device.

In other illustrative embodiments, a computer program product comprising a computer useable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided. The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectives and advantages thereof, will best be understood by reference to the following detailed description of illustrative embodiments when read in conjunction with the accompanying drawings, wherein:

FIG. 1 depicts a pictorial representation of an example distributed data processing system in which aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of an example data processing system in which aspects of the illustrative embodiments may be implemented;

FIG. 3 is an example block diagram of the primary operational elements of an application analysis mechanism in accordance with one illustrative embodiment;

FIG. 4 is an example diagram of a transducer which may be utilized by the mechanisms of the illustrate embodiments to generate a context free grammar;

FIG. 5 is an example diagram illustrating a static string analysis in accordance with one illustrative embodiment;

FIG. 6 is a call graph for an example program in accordance with one illustrative embodiment;

FIG. 7 is an example a block/flow diagram that illustratively depicts a system and method for static detection and categorization of information-flow downgraders in accordance with one illustrative embodiment; and

FIG. 8 is a flowchart outlining an example operation of an automated application analysis mechanism in accordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for static enforcement of Web application security guidelines by using formal analysis of the quality and conformance of information flow downgraders. In particular, the illustrative embodiments provide automated mechanisms for checking that downgraders in the code of an application, such as a Web application (i.e. an application accessible via the World Wide Web, or the Internet), and ensuring that the code of the application follows guidelines for implementing information flow downgraders. Based on this check, an output as to whether the Web application is in compliance with the guidelines or includes security violations may be generated.

The illustrative embodiments utilize string analysis mechanisms. String analysis is a collection of techniques and mechanisms whose goal is to approximate the set of runtime values that string variables may assume. Example applications of string analysis techniques to Web application security problems are described in co-pending and commonly assigned U.S. patent application Ser. Nos. 12/575,647; 12/825,293; and 12/627,351, which are incorporated herein by reference in their entirety. In particular, co-pending U.S. patent application Ser. No. 12/575,647 describes a technique for automated detection of information-flow downgraders using string analysis.

The mechanisms of the illustrative embodiments operate orthogonally to the mechanisms of the Ser. No. 12/575,647 application directed to how to find downgraders, e.g., sanitizers and validators, in application code. Instead, the mechanisms of the illustrative embodiments are directed to determining how the downgraders are structured and determining if the structure meets established guidelines. The mechanisms of the illustrative embodiments abstract away implementation details of the downgrader and represent the downgrader as a sequence of manipulations on the downgrader's inputs. This abstraction is accomplished by modeling built-in string operations, as well as other instructions of interest. For example, a monadic second-order logic model may be used to model the built-in string operations. The resulting high level abstracted representation of the string operations can then be examined and graded according to a set of guidelines. This may be performed as an automated procedure for quantifying the quality of the application's protection layer, i.e. the information flow downgraders of the application.

The automated mechanism for evaluating downgraders according to established guidelines can be used as an important bridge between application developers and security experts as it allows the security experts to validate code implementations of downgraders and communicate an evaluation to the application developers via a formal mechanism. The automated mechanisms of the illustrative embodiments further are able to identify downgraders whose scores are particularly low such that they may be the reason for business-logic bugs, e.g., if their low score is due to the fact that they eliminate input characters, instead of rejecting the input, which leads to unintended behaviors in business-logic.

As will be appreciated by one skilled in the art, the present invention may be embodied as a system, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in any one or more computer readable medium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Computer code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio frequency (RF), etc., or any suitable combination thereof.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java™, Smalltalk™, C++, or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the illustrative embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions that implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The illustrative embodiments may be utilized in many different types of data processing environments including a distributed data processing environment, a single data processing device, or the like. In order to provide a context for the description of the specific elements and functionality of the illustrative embodiments, FIGS. 1 and 2 are provided hereafter as example environments in which aspects of the illustrative embodiments may be implemented. It should be appreciated that FIGS. 1-2 are only examples and are not intended to assert or imply any limitation with regard to the environments in which aspects or embodiments of the present invention may be implemented. Many modifications to the depicted environments may be made without departing from the spirit and scope of the present invention.

With reference now to the figures, FIG. 1 depicts a pictorial representation of an example distributed data processing system in which aspects of the illustrative embodiments may be implemented. Distributed data processing system 100 may include a network of computers in which aspects of the illustrative embodiments may be implemented. The distributed data processing system 100 contains at least one network 102, which is the medium used to provide communication links between various devices and computers connected together within distributed data processing system 100. The network 102 may include connections, such as wire, wireless communication links, or fiber optic cables.

In the depicted example, server 104 and server 106 are connected to network 102 along with storage unit 108. In addition, clients 110, 112, and 114 are also connected to network 102. These clients 110, 112, and 114 may be, for example, personal computers, network computers, or the like. In the depicted example, server 104 provides data, such as boot files, operating system images, and applications to the clients 110, 112, and 114. Clients 110, 112, and 114 are clients to server 104 in the depicted example. Distributed data processing system 100 may include additional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is the Internet with network 102 representing a worldwide collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers, consisting of thousands of commercial, governmental, educational and other computer systems that route data and messages. Of course, the distributed data processing system 100 may also be implemented to include a number of different types of networks, such as for example, an intranet, a local area network (LAN), a wide area network (WAN), or the like. As stated above, FIG. 1 is intended as an example, not as an architectural limitation for different embodiments of the present invention, and therefore, the particular elements shown in FIG. 1 should not be considered limiting with regard to the environments in which the illustrative embodiments of the present invention may be implemented.

With reference now to FIG. 2, a block diagram of an example data processing system is shown in which aspects of the illustrative embodiments may be implemented. Data processing system 200 is an example of a computer, such as client 110 in FIG. 1, in which computer usable code or instructions implementing the processes for illustrative embodiments of the present invention may be located.

In the depicted example, data processing system 200 employs a hub architecture including north bridge and memory controller hub (NB/MCH) 202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204. Processing unit 206, main memory 208, and graphics processor 210 are connected to NB/MCH 202. Graphics processor 210 may be connected to NB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connects to SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive 230, universal serial bus (USB) ports and other communication ports 232, and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus 240. PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM 224 may be, for example, a flash basic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD 226 and CD-ROM drive 230 may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating system coordinates and provides control of various components within the data processing system 200 in FIG. 2. As a client, the operating system may be a commercially available operating system such as Microsoft® Windows®7 (Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both). An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java™ programs or applications executing on data processing system 200 (Java is a trademark of Oracle and/or its affiliates in the United States, other countries, or both).

As a server, data processing system 200 may be, for example, an IBM® eServer™ System p® computer system, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system (eServer, System p, and AIX are trademarks of International Business Machines Corporation in the United States, other countries, or both while LINUX is a trademark of Linus Torvalds in the United States, other countries, or both). Data processing system 200 may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit 206. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD 226, and may be loaded into main memory 208 for execution by processing unit 206. The processes for illustrative embodiments of the present invention may be performed by processing unit 206 using computer usable program code, which may be located in a memory such as, for example, main memory 208, ROM 224, or in one or more peripheral devices 226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may be comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem 222 or network adapter 212 of FIG. 2, may include one or more devices used to transmit and receive data. A memory may be, for example, main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardware in FIGS. 1-2 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in FIGS. 1-2. Also, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system, other than the SMP system mentioned previously, without departing from the spirit and scope of the present invention.

Moreover, the data processing system 200 may take the form of any of a number of different data processing systems including client computing devices, server computing devices, a tablet computer, laptop computer, telephone or other communication device, a personal digital assistant (PDA), or the like. In some illustrative examples, data processing system 200 may be a portable computing device which is configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data, for example. Essentially, data processing system 200 may be any known or later developed data processing system without architectural limitation.

One or more of the computing devices in FIGS. 1 and 2 may be used to implement aspects of the illustrative embodiments. For example, a server computing device, such as server 104 in FIG. 1, may be used to implement a Web application that may be accessible by one or more client computing devices, such as clients 110, 112, and 114. Before deploying the Web application, or after implementation of the Web application by the server 104, the mechanisms of the illustrative embodiments may be employed to perform analysis of Web application to determine if the downgrader(s) implemented by the Web application, or in conjunction with the Web application, comply with security guidelines. The mechanisms for performing the analysis of the Web application may be implemented in a server computing device, such as, for example, the server 104 upon which the Web application is to be deployed or is already implemented, a client computing device 110, 112, or 114, another computing device, a plurality of computing devices, or the like.

FIG. 3 is an example block diagram of the primary operational elements of an application analysis mechanism in accordance with one illustrative embodiment. The elements shown in FIG. 3 may be implemented in software, hardware, or any combination of software and hardware. In one illustrative embodiment, the elements of FIG. 3 are implemented as software instructions executed by one or more processors of one or more computing devices.

As shown in FIG. 3, the application analysis mechanism 300 comprises a controller 310, an input/output interface 320, a downgrader detection engine 330, a downgrader abstraction engine 340, a downgrader evaluation engine 350, and a security guidelines engine 360. The controller 310 controls the overall operation of the application analysis mechanism 300 and orchestrates the operation of each of the other elements 320-360. The input/output interface 320 operates to receive inputs, such as Web application code for analysis, user inputs (if any) for configuring the operation of the application analysis mechanism 300, and the like. The input/output interface 320 operates to further generate outputs for providing notifications to developers, security personnel, or other users to inform them of whether or not a Web application's downgraders meet established security guidelines or not, and if not, what violations may be present.

The downgrader detection engine 330 analyzes application code to detect in the application code where downgrader code may be present. In one illustrative embodiment, the downgrader detection engine 330 may locate downgrader code within application code using string analysis in a manner such as described in co-pending and commonly assigned U.S. patent application Ser. No. 12/575,647 (hereafter referred to as the '647 application). Of course, other known, or later developed, mechanisms for detecting downgrader code within application code may be used without departing from the spirit and scope of the illustrative embodiments.

In order to provide one example of how downgrader code may be identified within application code, it is assumed that the string analysis of the '647 application is utilized. In such a string analysis, application code is first translated into a context-free grammar (CFG) with string operations in a manner such as described in Yasuhiko Minamide, “Static Approximation of Dynamically Generated Web Pages,” Proceedings of the 14^(th) International Conference on World Wide Web (WWW '05), 2005), hereby incorporated by reference. The string operations are then removed from the context-free grammar to generate production rules by applying approximated string operation functions over CFGs which correspond to the string operations in the original application code. These production rules are typically denoted by the form N→X1 . . . Xn, where N is a variable and X1 . . . Xn are variables or characters and constitute the context-free grammar. The approximated string operation functions are functions that translate a CFG to another CFG, e.g., a transducer that is a finite state automaton with output symbols. Application of the approximated string operations is iterated until there are no more string operations in the production rules. This results in a context-free grammar that consists of the production rules without string operations.

That is, a program is represented as a context-free grammar, where the starting variable corresponds to the string you are interested in approximating. Suppose, for example, that this string is v in: “v=v1.concat(v2);”. Computing the abstract value of v requires knowledge of the abstract values of v1 and v2, and moreover, the “concat” operation needs to be removed from the grammar by modeling its effect on v1 and v2 once their values are known. This is what's meant above by stating that application of the approximated string operations is iterated until there are no more string operations in the production rules.

Suppose also that it is learned that v depends on v1 and v2, so the approximation may considering v1 and v2 as the starting variables recursively (each in turn). The original production rule is not necessary for obtaining the abstract values of these strings (unless there's a cyclic definition as discussed further below). Once this recursive consideration of v1 and v2 is performed, the operation returns to the grammar for v, and applies a transducer approximating the effect of “concat”. This yields a simplified grammar, where the “concat” operation is no longer present, and in fact, the rule simply assigns a value to v. Fixpoint iteration, rather than the above described recursion, is needed to handle cyclic definitions, e.g., if the concatenation is done in a loop: The value of v at the i-th iteration depends on its value in the (i−1)-th iteration.) As a further example, consider the following Java™ program which appends “a” to the string assigned to the variable a three times after initializing it with the string value “a.”

-   -   string a=“a”;     -   for (int i=0; i<3; i++)         -   a=a+“a”;     -   string r=a;

The following CFG is obtained by translating every application variable v into a nonterminal string variable S_(v) and “=” into a→ as in production rules, where the string concatenation by + is considered to be a string concatenation on the CFG.

-   -   S_(a)→a     -   S_(a)→S_(a)a     -   S_(r)→S_(a)

For example, the CFG with start symbol S_(a) represents a set of possible string values assigned to the application variable a, which yields the set of strings {“a”,“aa”,“aaa”,“aaaa”, . . . }. Likewise, the symbol S_(r) represents a set of possible string values assigned to the program variable r. It contains strings that are never assigned to the variables a and r, since the string analysis completely ignores the condition of the “for” statement.

When the application code uses predefined string operations, such as String.substring as shown in the following portion of application code, a sound approximation is used for every string operation to translate a application code into a CFG. For example, consider the predefined string operation in the following portion of code:

-   -   String a=“xxa”;     -   for (int i=0; i<3; i++) a=a+“a”;     -   String r=a.substring(2);         Soundness of the approximated string operation means that a         resulting CFG computed by the approximated string operation         contains all the actual strings computed by a predefined string         operation that corresponds to the approximated string operation.         One of the methods to approximate the predefined string         operation is to use a transducer which is an automaton with         output.

Soundness may be formally defined as follows; f is a sound approximation for a string operation f iff S′⊂ f_(S) where S″=(s′|s′=f(s), sεS). One of the methods to approximate predefined string operations is to use a transducer which is an automaton with output. It is well known that the image of a transducer is also a CFG. Other methods are homomorphisms on (Σ, +) where Σ is a set of characters and + denotes concatenation functions that always return the same CFG yielding all the possible strings returned by corresponding predefined string operations, and so on. The following production rules with the approximated string operation substring(_(—),2) are the ones obtained from the example portion of application code above by using sound approximation:

-   -   S_(a)→xxa     -   S_(a)→S_(a)a     -   S_(r)→substring(S_(a),2)

Referring now to FIG. 4, a transducer 400 is illustratively depicted. An approximated string operation substring(_(—),2) is defined by the transducer 400 and is a sound approximation for the string operation substring(_(—),2). By applying the depicted transducer 400 to the grammar consisting of the above production rules, one can obtain the following CFG which represents the set {“a”,“aa”,“aaa”,“aaaa”, . . . }.

-   -   S′_(a)→a     -   S′_(a)→S′_(a)a     -   S_(r)→S′_(a)

The symbol A in FIG. 4 represents any character while A/ε represents a transformation of any one character to the empty string, which means removing the character. With a “sound” transducer, the resulting abstract value is a safe approximation of the strings that may result from the operation at run-time. Thus, for “substring(_(—),2)”, the representation simply removes the first two characters in the string, as indicated by the labels A/ε (essentially removes character A). A new CFG is obtained which results from applying the transducer to the original grammar.

After iteratively applying the above mechanisms to the application code, a set of product rules representing the application, but in which no string operations are present, is obtained. Thus, a context-free grammar consisting of the resulting production rules is the outcome of the string analysis.

Referring to FIG. 5, one illustrative embodiment will now be described for implementing a static string analysis in accordance with the illustrative embodiments. The description provided deals with intra-procedural string analysis, and then explains how to extend that to inter-procedural string analysis. The implementation details presented assume that the string analysis has been implemented on top of a static analysis framework. The static analysis framework employed may be any known framework and may include, for example, the Watson Libraries for Analysis (WALA), available as an open source product from wala.sourceforge.net.

To describe the intra-procedural string analysis, consider the nappend method in Java™ which is as follows:

public class MyClass { static public void main(String args[ ]) { String a = “a”; String b = “b”; String r = nappend(a, b, 3); } public void nappend(String x, String y, int n) { String r = null; if (n == 0) { r = x; } else { r = nappend(x + y, y, n−1); } return r; } }

In block 502, a translation of a program is made into Static Single Assignment (SSA) form, where pseudo notations are used for instructions. An example, translation is illustratively shown as follows:

main(String)

1. a=“a”

2. b=“b”

3. r=nappend(a, b, 3)

nappend(String)

1. b1=n==0

3. v1=x+y

4. r1=nappend(v1, y, n−1)

5. goto 8

6. r2=x

7. goto 8

8. r=phi(r1,r2)

9. return r

A call graph for this program is depicted in FIG. 6. Pseudo notations used for instructions in the above program include v=val for an assignment of a value val to a variable or a field v, v=obj.func(v1, . . . , vn) for a method invocation with arguments v1, . . . , vn, goto N for an unconditional jump to the label N, goto N if v for a conditional jump to the label N by the condition v. In addition, the SSA transformation introduces new variables and a Φ-function, which is denoted by phi(v1,v2), to yield a program which has only one assignment to each variable. This characteristic of the SSA form is suitable to find data dependencies.

In block 504, the assignments in SSA form are translated to a set of production rules with string operations 506, except for conditional and unconditional jumps, in the same manner described above (See FIG. 1 above). In particular, v=phi(v1,v2) is translated into two production rules S_(v)→S_(v1) and S_(v)→S_(v2) so as to make it represent a union of the two sets of strings assigned to v1 and v2, respectively. According to this translation, one can obtain the following production rules from the pseudo SSA form of the nappend method.

-   -   S_(v1)→S_(x)S_(y)     -   S¹ _(r)→nappend(S_(v1),S_(y),n−1)     -   S_(r2)→S_(x)     -   S_(r)→S_(v1)     -   S_(r)→S_(r1)

For the inter-procedural string analysis, the intra-procedural string analysis is extended with the call graph information constructed by WALA, whose context-sensitivity can be flexibly controlled by known methods. Every variable in the SSA program is annotated with a call graph node. All the production rules are combined after removing production rules translated from method invocations such as S_(r) ¹→nappend (S_(v1),S_(y),n−1). Production rules representing dependencies between the parameters and the return value of a callee method and the variables of a caller method are introduced. For example, the following production rules are introduced if a context-insensitive call graph 600 as shown in FIG. 6 is present, where the superscript of each non-terminal represents the corresponding call graph node. The production rules are:

S_(x) ² → S_(a) ¹ S_(x) ² → S_(v1) ² S_(y) ² → S_(b) ¹ S_(y) ² → S_(y) ² S_(r) ¹ → S_(r) ² S_(r1) ² → S_(r) ²

A complete set of the production rules with string operations 506 obtained from the program includes:

S_(a) ¹ → a S_(x) ² → S_(a) ¹ S_(b) ¹ → b S_(y) ² → S_(b) ¹ S_(v1) ² → S_(x) ²S_(y) ² S_(r) ¹ → S_(r) ² S_(r2) ² → S_(x) ² S_(x) ² → S_(v1) ² S_(r) ² → S_(r1) ² S_(y) ² → S_(y) ² S_(r) ² → S_(r2) ² S_(r1) ² → S_(r) ²

An optional pointer analysis may be performed that helps the string analyzer or solver 308 to identify how constant strings flow to variables across methods and to identify whether the same objects are assigned to different variables in potentially different methods, even if those objects are dynamically created. In block 510, the following CFG is obtained that predicts possible strings assigned to the variable r in the main method, where the start symbol is S_(r) ¹.

-   -   S¹ _(r)→a|S¹ _(r)b

Referring to FIG. 7, a block/flow diagram illustratively depicts a system and method for static detection and categorization of information-flow downgraders in accordance with the illustrative embodiments. The system and method shown in FIG. 7 may be implemented in the downgrader detection engine 330 in FIG. 3, for example, for detecting portions of code in application code that correspond to downgraders.

As shown in FIG. 7, in block 702, a program stored in a memory device, which may have been provided from a separate device via the input/output interface 320 in FIG. 3, for example, is transformed by statically analyzing program variables to yield a single assignment to each variable in an instruction set. This includes transforming the application code by employing pseudo notations for program variable assignments.

In block 704, the instruction set is translated to production rules with string operations in a manner as described above, for example. In block 706, a pointer analysis, as is generally known in the art, is optionally performed on the production rules with string operations to improve precision.

In block 708, a context-free grammar is generated from the production rules. In block 710, an information-flow downgrader function is identified by checking the context-free grammar against one or more function specifications. The one or more function specifications consist of a string pattern, such as a regular expression. This string pattern is given by an analysis tool or a user, and it represents a set of strings which may (or may not) be used in a security-sensitive operation. For example, the strings “<” and “>” are considered to be unsafe strings since these characters are typically used by XSS (cross-site scripting) attacks. Therefore, regular expressions “.*[< >].*” (i.e. specifying that the “<” and “>” strings are not safe) may be part of a specification for an information-flow downgrader that is used to avoid the XSS attacks.

The one or more functions preferably include a security-sensitive function in the application code. This may include detecting and categorizing the downgrader functions based upon a purpose of the downgrader function.

In block 712, the context free grammar is preferably compared with a specification of an information-flow downgrader, such that if the grammar satisfies the specification, the input is considered properly downgraded. Comparison between a context-free grammar and a specification of a regular expression can be done, for example, by a CFL reachability algorithm, as described in the paper “Program Analysis via Graph Reachability” by Thomas Reps, University of Wis., August 1998.

The one or more function specifications are employed to categorize the downgrader function as being a correct downgrader for one or more security vulnerabilities, for example. It is assumed that there are multiple string patterns (regular expressions) each of which is corresponding to a security vulnerability, such as cross-site scripting (XSS) and HTTP Response Splitting (HRS). When there are string patterns for XSS and HRS, for example, the obtained context-free grammar is compared with those two string patterns. If the intersection of the context-free grammar with the XSS pattern is not empty, the corresponding function is detected as a correct information flow downgrader for XSS, where the intersection can be computed by the CFL reachability algorithm mentioned above, for example. Likewise, if the intersection of the context-free grammar and the HRS pattern is not empty, the corresponding function is detected as a correct information flow downgrader for HRS. It should be noted that the function may be detected as a correct information flow downgrader for both XSS and HRS as well in this example. Of course the present invention is not limited to XSS and HRS, but may be used with any known or later developed security vulnerabilities.

Another type of string analysis that may be employed, and which is used in illustrative embodiments of the present invention, uses a monadic second-order abstraction of the downgraders to perform string analysis. Referring again to FIG. 3, having identified one or more downgraders in the application code in some manner, such as the manner described above, the downgrader abstraction engine 340 is employed to build an abstract representation of the downgraders. That is, the downgrader abstraction engine 340 may be applied to each downgrader identified in the application code so that a separate abstract representation of the downgrader is generated to model the downgrader.

In one illustrative embodiment, the downgrader abstraction engine 340 may implement monadic second-order logic on strings in the downgrader where the downgrader's code is represented as a formula in monadic second-order logic. An example implementation of monadic second-order logic as applied to strings is described in Elgaard et al., “MONA 1.x: New Techniques for WS1S and WS2S,” available from www.brics.dk/mona/papers/mona1x-new-technique-ws1s-ws2s/article.pdf, which is hereby incorporated by reference herein.

With this monadic second-order logic, instructions in the downgrader code that do not manipulate the input to the downgrader code are either ignored, if possible, or represented in a way that reflects the instructions' influence on the downgrader's result. For example, a test of the form “if (input.contains(”xyz“)){ . . . }” is represented by asserting, inside the body of the condition, that the input contains string “xyz.”

Again referring to FIG. 3, using the abstract representation generated by the downgrader abstraction engine 340, the downgrader evaluation engine 350 evaluates the way in which the downgrader processes the input to the downgrader code in the application code, e.g., evaluating the way in which a sanitizer operates to transform or remove/replace suspect parts of the input or the way in which a validator judges whether an input is illegal or not and determines whether or not to reject the input. This evaluation involves comparing the downgrader processing of input against a specification, such as a set of formal security guidelines specifying how downgraders should operate. For example, the security guidelines engine 360 may provide pre-established security guidelines that set forth a set of rules and conditions under which downgraders are required to operate in order to avoid any security issues with the operation of application code. These pre-established security guidelines may be defined by security personnel and set forth in a database or other data structure associated with the security guidelines engine 360. The security guidelines engine 360 may provide one or more user interfaces through which such security personnel or other users may define and store such security guidelines in the database or data structure.

For example, assume that a pre-established security guideline for the downgraders is that inputs to the downgrader should either be accepted or rejected (e.g., such as is done by a validator), but not mutated by the downgrader (e.g., as is done by a sanitizer). This security guideline prefers or favors validators over sanitizers. Functionally, both a validator and sanitizer may be equally correct for ensuring the security of the application, however, a sanitizer may be more difficult to reason about (e.g., the side effects of a replacement operation performed by a sanitizer need to be considered) and more problematic in terms of user expectations (e.g., the change that the sanitizer performs may invalidate certain logic aspects of the input).

Considering an abstract representation of the downgrader as generated by the downgrader abstraction engine 340, a static-analysis mechanism 352 of the downgrader evaluation engine 350 interprets the effect of the operations the downgrader performs on its input and determine whether it is the case that the input is mutated, e.g., the downgrader implements a sanitizer. If so, and a security guideline rule is in place and provided by the security guidelines engine 360 that indicates that inputs to the downgrader are not to be mutated, then the downgrader evaluation engine 350 may generate an output indicating that a violation of a security guideline is present in the evaluated downgrader. This output may be a notification message sent to a designated contact address, an output generated on a client computing device's output device, be it a display device, printer, etc., or the like. The notification message may indicate the application, the security guideline violated, and the location within the application code where the security guideline violation was detected, i.e. what downgrader violated the security guideline, for example.

Moreover, the downgrader evaluation engine 350 may comprise logic for identifying the violation as well as checking a knowledge base 354 associated with the downgrader evaluation engine 350 to determine recommendations for modifying the downgrader code to make the downgrader in compliance with established security guidelines. For example, if it is determined that the downgrader mutates the input to generate a sanitized output from the downgrader, and the above security guideline is in place, then the downgrader evaluation engine 350 may check the knowledge base 354 based on the detected violation as well as other information about the structure of the downgrader code which may be extracted using the static-analysis mechanisms, and recommend that the downgrader code be modified to replace the sanitization elements of the downgrader code that mutate the input with portions of code that either accept or reject the input without mutating the input.

In some illustrative embodiments, the downgrader evaluation engine 350 may further include downgrader scoring logic 356. The downgrader scoring logic 356 operates to identify a degree to which a downgrader diverges from the established security guidelines provided by the security guidelines engine 360 and quantify this degree of divergence to generate a score for the downgrader. For example, the score may be simply a count of a number of security guidelines violated by the downgrader code, a weighted count of a number of security guidelines violated where the weight is associated with the particular security guidelines violated to thereby indicate a priority of security guidelines, or the like.

The score for a downgrader, once calculated in the manner described above based on its divergence from established security guidelines, may be compared to one or more established threshold values to obtain an indication of which downgraders are considered to represent mild, moderate, or significant security threats to the application, for example. Various processing can be performed based on this categorization of downgraders into security threat levels. For example, downgraders representing a mild security threat may simply be identified and a notification sent to an appropriate user to inform them of the potential security issues associated with the downgrader. Downgraders that represent a moderate and significant security threat may have higher priority notifications generated and sent to one or more users and may include further information regarding suggested modifications to the downgrader code to make the downgrader in compliance with established security guidelines.

In some illustrative embodiments, the downgrader evaluation engine 350 may automatically modify the downgrader code based on the evaluation of the downgrader with regard to established security guidelines so as to place the downgrader in compliance with the security guidelines or at least lessen the security threat posed by the downgrader. For example, in one illustrative embodiment, if a downgrader is determined to be a sanitizer and the downgrader is determined to be a significant security threat, the downgrader evaluation engine 350 may automatically modify the code of the downgrader to change it to a validator to thereby reduce the security threat. The modified code may then be provided to an appropriate user such that it may then be recompiled before deployment on a computing device.

Thus, the mechanisms of the illustrative embodiments not only provide a mechanism for identifying downgrader code within application code, but also provide mechanisms for abstracting this downgrader code such that the downgrader processes may be evaluated against established security guidelines. In this way, an automated mechanism is provided for ensuring that downgraders provide a minimum level of security with regard to the application as defined by the security guidelines. The mechanisms of the illustrative embodiments are able to model operations of a downgrader using string-analysis in a way that conservatively captures the concrete semantics of the operations and come to a conclusion as to whether a downgrader meets the minimum security guidelines automatically.

FIG. 8 is a flowchart outlining an example operation of an automated application analysis mechanism in accordance with one illustrative embodiment. As shown in FIG. 8, the operation starts with the receipt of application code to be analyzed (step 810). The static-analysis mechanisms are applied to the application code to identify downgrader code within the application code (step 820). For a next downgrader identified in step 820, an abstract representation of the downgrader is generated (step 830). The downgrader processes are then evaluated against established security guidelines using the abstract representation of the downgrader (step 840). A determination is made as to whether there are any detected violations of the security guidelines based on this evaluation (step 850). If so, optionally, the degree of the violation, or deviation of the downgrader processes from the security guidelines, is quantified so as to generate a score for the downgrader (step 860). The score may then be compared against one or more thresholds to thereby categorize the severity of the downgrader violation of security guidelines (step 870). In addition, optionally, a knowledge base may be accessed to identify a recommended solution to the security guideline violation so as to bring the downgrader into compliance with the security guidelines (step 880).

Based on the detection of the violation, and optionally the score and/or information obtained from the knowledge base, a notification message regarding the security guideline violation may be created and output (step 890). Thereafter, a determination is made as to whether there are additional downgraders in the application code to process (step 895). If so, the operation returns to step 830; otherwise the operation terminates.

It should be noted that while the above illustrative embodiments describe one mechanism for identifying downgraders within application code, such as a Web application, the illustrative embodiments are not limited to this one mechanism. To the contrary, any mechanism for locating portions of code within application code, that corresponds or performs a downgrader function may be used without departing from the spirit and scope of the illustrative embodiments. Moreover, while the illustrative embodiments are described as being applied to a Web application and the information flow associated with a Web application, they are not limited to such. Rather the illustrative embodiments may be utilized with any application code that implements downgraders in the application code or in association with the application code.

As noted above, it should be appreciated that the illustrative embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In one example embodiment, the mechanisms of the illustrative embodiments are implemented in software or program code, which includes but is not limited to firmware, resident software, microcode, etc.

A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters.

The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 

What is claimed is:
 1. A method, in a data processing system, for evaluating downgrader code in application code with regard to one or more security guidelines, comprising: identifying, by an application analysis mechanism of the data processing system, the downgrader code in application code, wherein the downgrader code is a portion of code in the application code that operates on an information flow of the application code to ensure confidentiality of information input to the downgrader code, in the output of the downgrader code; determining, by the application analysis mechanism, if the one or more processes of the downgrader code violate the one or more security guidelines, at least by evaluating the one or more processes of the downgrader code against one or more security guidelines; generating, by the application analysis mechanism, a notification in response to the evaluation indicating that the one or more processes of the downgrader code violate the one or more security guidelines; and outputting, by the application analysis mechanism, the notification.
 2. The method of claim 1, wherein identifying the downgrader code comprises: translating application code into a context-free grammar with string operations; removing the string operations from the context-free grammar using approximated string operation functions to generate a context-free grammar without string operations; and comparing the context-free grammar without string operations to one or more string patterns to identify downgrader code within the application code.
 3. The method of claim 1, further comprising creating an abstraction of the downgrader code identified in the application code, wherein evaluating one or more processes of the downgrader code against the one or more security guidelines to determine if the one or more processes of the downgrader violate the one or more security guidelines is performed using the abstraction of the downgrader code as a representation of the one or more processes of the downgrader code.
 4. The method of claim 3, wherein the abstraction is a monadic second-order abstraction in which monadic second-order logic is applied to strings in the downgrader code.
 5. The method of claim 1, wherein the one or more security guidelines comprise rules and conditions under which downgraders are required to operate in order to avoid security issues with the operation of the application code.
 6. The method of claim 1, wherein the notification identifies the application code, a security guideline violated by the downgrader, and a location within the application code where the security guideline violation was detected.
 7. The method of claim 1, further comprising: checking a knowledge base data structure based on a detected violation of a security guideline to identify a recommendation for modifying the downgrader code to correct the detected violation of the security guideline, wherein the notification includes the recommendation.
 8. The method of claim 7, further comprising: automatically applying the recommendation to the downgrader code to thereby generate modified downgrader code in the application code.
 9. The method of claim 1, further comprising: generating a score for a downgrader based on a degree to which a downgrader diverges from established security guidelines; comparing the score to one or more predetermined threshold values; and generating an indication of a security threat level of the downgrader code based on the comparison of the score to the one or more predetermined threshold values, wherein the indication of the security thread level is included in the notification.
 10. The method of claim 9, wherein the score is a weighted sum of a number of the one or more security guidelines violated by the downgrader code.
 11. The method of claim 1, wherein the downgrader is one of a sanitizer or a validator.
 12. The method of claim 11, wherein: in response to the downgrader being a sanitizer, evaluating one or more processes of the downgrader code against the one or more security guidelines comprises evaluating an operation of the downgrader with regard to transforming, removing, or replacing suspect parts of an input to determine if the one or more security guidelines are violated by the operation of the downgrader, and in response to the downgrader being a validator, evaluating one or more processes of the downgrader code against the one or more security guidelines comprises evaluating an operation of the downgrader with regard to determining whether an input is illegal or not and rejecting illegal inputs. 